Today I worked on my project. Initially, I embarked on this project with a basic understanding of machine learning and data analysis. However, as I progressed, I realized that simply training a model on a dataset and I learnt depth about the P-value and T-test.
I learnt about the Cross validation and validation set approach. One crucial component of cross-validation is the validation set. This set is used to assess the model’s performance during training and fine-tuning. It acts as a sort of checkpoint, helping me adjust the model’s hyperparameters and detect any issues early in the development process. By separating a portion of my dataset for validation, I gained a clearer understanding of how well my model was learning from the data.